Machine Learning

Predicting the Vote Using Legislative Speech

foaad — Sun, 14 Aug 2022 03:16:16 +0000

Predicting the Vote Using Legislative Speech foaad Sat, 08/13/2022 - 20:16

Authorship

Aditya Budhwar, Alex Dekhtyar and Foaad Khosmood

Publication

Proceedings of the 19th Annual International Conference on Digital Government Research: Governance in the Data Age

Conference

Location

Delft, Netherlands

Wednesday, May 30, 2018 - 12:00

Tags

ACM DL

Abstract

As most dedicated observers of voting bodies like the U.S. Supreme Court can attest, it is possible to guess vote outcomes based on statements made during deliberations or questioning by the voting members. We show this is also possible to do automatically using machine learning, potentially providing a powerful tool to ordinary citizens. Our working hypothesis is that verbal utterances made during the legislative process by elected representatives can indicate their intent on a future vote, and therefore can be used to automatically predict said vote to a significant degree.

Computational Style Processing (Ph.D. Dissertation)

foaad — Thu, 11 Aug 2022 15:47:56 +0000

Computational Style Processing (Ph.D. Dissertation)

foaad Thu, 08/11/2022 - 08:47

Authorship

Foaad Khosmood

Publication

UC Santa Cruz Division of Graduate Studies

Non refereed paper

Location

Santa Cruz, California

Thursday, December 1, 2011 - 12:00

Tags

ACM DL

Abstract

Our main thesis is that computational processing of natural language styles can be accomplished using corpus analysis methods and language transformation rules. We demonstrate this first by statistically modeling natural language styles, and second by developing tools that carry out style processing, and finally by running experiments using the tools and evaluating the results.

Diss9.pdf

Digital Democracy Project: Making Government More Transparent one Video at a Time

foaad — Sat, 21 Nov 2015 05:44:33 +0000

Digital Democracy Project: Making Government More Transparent one Video at a Time

foaad Fri, 11/20/2015 - 21:44

Authorship

Sam Blakeslee, Alex Dekhtyar, Foaad Khosmood, Franz Kurfess, Toshihiro Kuboi, Hans Poschman, Giovanni Prinzivalli, Christine Robertson, Skylar Durst

Publication

Digital Humanities 2015

Poster or Panel Discussion

Location

Sydney, Australia

Wednesday, July 1, 2015 - 12:00

Tags

Archived Abstract

Abstract

The Digital Democracy platform obtains data about the legislative committee hearings: the video archives, the information about the state legislature and so on. Figure 1 shows the design of the DD system. The main source of information for the DD platform is the Cal Channel video archive of legislative sessions, a service provided courtesy of cable TV companies that operate in California.

The Digital Democracy Project- poster - DH2015.pdf

Digital Democracy Poster (DH 2015).pdf

Comparison of Sentence-level Paraphrasing Approaches for Statistical Style Transformation

foaad — Wed, 22 Aug 2012 05:48:08 +0000

Comparison of Sentence-level Paraphrasing Approaches for Statistical Style Transformation foaad Tue, 08/21/2012 - 22:48

Authorship

Foaad Khosmood

Publication

International Conference on Artificial Intelligence (ICAI)

Conference

Location

Las Vegas, Nevada

Sunday, July 1, 2012 - 12:00

Tags

ICA3949.pdf

Combining Corpus-Based Features for Selecting Best Natural Language Sentences

foaad — Wed, 22 Aug 2012 05:43:15 +0000

Combining Corpus-Based Features for Selecting Best Natural Language Sentences foaad Tue, 08/21/2012 - 22:43

Authorship

Foaad Khosmood and Robert Levinson

Publication

International Conference on Machine Learning and Applications

Conference

Location

Honolulu, Hawaii

Thursday, December 1, 2011 - 12:00

Tags

Abstract

Automated paraphrasing of natural language text has many interesting applications from aiding in better translations to generating better and more appropriate style language. In this paper, we are concerned with the problem of picking the best English sentence out of a set of machine generated paraphrase sentences, each designed to express the same content as a human generated original. We present a system of scoring sentences based on examples in large corpora.

4607b362.pdf

Taxonomy and Evaluation of Markers for Computational Stylistics

foaad — Wed, 22 Aug 2012 05:30:32 +0000

Taxonomy and Evaluation of Markers for Computational Stylistics foaad Tue, 08/21/2012 - 22:30

Authorship

Foaad Khosmood and Robert Levinson

Publication

International Conference on Artificial Intelligence (ICAI)

Conference

Location

Las Vegas, Nevada

Friday, July 1, 2011 - 12:00

Tags

Abstract

Currently, stylistic analysis of natural language texts is achieved through a wide variety of techniques containing many different algorithms, feature sets and collection methods. Most machine-learning methods rely on feature extraction to model the text and perform classification. But what are the best features for making style based distinctions? While many researchers have developed particular collections of style features – called style markers – no definitive list exists.

icai-11-kl-final.pdf

Automatic Synonym and Phrase Replacement Shows Promise for Style Transformation

foaad — Wed, 22 Aug 2012 05:24:05 +0000

Automatic Synonym and Phrase Replacement Shows Promise for Style Transformation foaad Tue, 08/21/2012 - 22:24

Authorship

Foaad Khosmood and Robert Levinson

Publication

IEEE International Conference of Machine Learning and Applications

Conference

Location

Washington, DC

Wednesday, December 1, 2010 - 12:00

Tags

Abstract

Style transformation refers to the process by which a piece of text written in a certain style of writing is transformed into another text exhibiting a distinctly different style of writing without significant change to the meaning of individual sentences. In this paper we continue investigation into the linguistic style transformation problem and demonstrate current achievements in transformation on sample texts from a standard authorship attribution corpus.

icmla10-camera.pdf

Toward Automated Stylistic Transformation of Natural Language Text

foaad — Wed, 22 Aug 2012 04:55:25 +0000

Toward Automated Stylistic Transformation of Natural Language Text foaad Tue, 08/21/2012 - 21:55

Authorship

Foaad Khosmood and Robert Levinson

Publication

Digital Humanities

Conference

Location

University of Maryland College Park

Monday, June 1, 2009 - 12:00

Toward Unification of Source Attribution Processes and Techniques

foaad — Wed, 22 Aug 2012 04:50:31 +0000

Toward Unification of Source Attribution Processes and Techniques foaad Tue, 08/21/2012 - 21:50

Authorship

Foaad Khosmood and Robert Levinson

Publication

IEEE International Conference on Machine Learning and Cybernetics (ICMLC)

Conference

Location

Dalian, China

Tuesday, August 1, 2006 - 12:00